In many fields, it is important to be able to analyse sound in order to classify and identify different types of sound. For example, in the car and aviation industry it is often necessary to classify sounds from machines and engines in order to diagnose possible problems, such as cracks or worn bearings in the machine or engine. Speech recognition is another field where reliable analysis of sound is required. Today, a few different methods are employed for speech identification. A device for speech recognition is described in U.S. Pat. No. 5,285,522. Here, neural nets are used to recognise different voice patterns. The input signals to the neural nets come from a set of bandpass filters which separate the input acoustic patterns into frequency ranges. Another system for speech recognition is disclosed in U.S. Pat. No. 5,377,302, In this case, too, a neural net is employed, whose input signal consists of a signal filtered in multiple stages. The object of that system is to improve recognition of phonemes from speech. Both systems have difficulty analysing sound signals since they are not capable of distinguishing the magnitudes of the signals which are relevant to sound analysis. The reason for this is mainly that the processing of the signal prior to the neural filter cuts out important information and that the degrees of freedom are too limited. In addition, there are systems which work with neural nets only. However, in such systems, the neural nets cannot perform their task because the degrees of freedom are too many. It has become apparent that a certain amount of processing of the signal is required prior to it being fed to a neural net. Consequently, the neural nets do not have the intended effect in the known systems since they receive either too much or too little information about the signals. The same drawbacks as the ones associated with speech recognition would arise if one were to attempt to use the systems mentioned above for classification of machine or engine sounds.
The method most commonly employed today for classifying such sounds consists of simply listening to the machines with a stethoscope. Often, an experienced and trained ear has proven to be the most reliable tool for classifying and identifying various malfunction sounds and dissonances in, for example, an engine. Obviously, this method of classification has a number of drawbacks. Relying on a person's hearing is, of course, risky in itself since, for one thing, our hearing changes with time. In addition, it takes a long time for a person to bring her ability to recognise sound up to a level where misinterpretations are avoided to the greatest extent possible. Consequently, it is obvious that there is a need for a device which obviates the above-mentioned drawbacks of the prior art and which can analyse and filter sound in order to identify and classify accurately and reliably different sound types and sound patterns.